469 lines
16 KiB
Python
469 lines
16 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from abc import ABC, abstractmethod
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from collections import defaultdict
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from collections.abc import Sequence
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from pathlib import Path
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from typing import (TYPE_CHECKING, Any, Callable, Generic, NamedTuple,
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Optional, TypeVar, Union)
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from torch import nn
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from vllm.inputs import InputContext
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from vllm.logger import init_logger
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from vllm.utils import (ClassRegistry, get_allowed_kwarg_only_overrides,
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resolve_mm_processor_kwargs)
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if TYPE_CHECKING:
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from vllm.config import ModelConfig
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from vllm.sequence import SequenceGroupMetadata
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from .inputs import (ModalityData, MultiModalDataDict, MultiModalKwargs,
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PlaceholderRange)
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logger = init_logger(__name__)
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MultiModalInputMapper = Callable[[InputContext, ModalityData[object]],
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MultiModalKwargs]
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"""
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Return a dictionary to be passed as keyword arguments to
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:meth:`~torch.nn.Module.forward`. This is similar in concept to tokenizers
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and processors in HuggingFace Transformers.
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If the data is not supported, throw :exc:`TypeError`.
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"""
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MultiModalTokensCalc = Union[int, Callable[[InputContext], int]]
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"""
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Calculate the maximum number of multimodal tokens input to the language
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model. This does not include tokens that correspond to the input text.
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"""
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_T = TypeVar("_T")
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N = TypeVar("N", bound=type[nn.Module])
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class MultiModalPlugin(ABC):
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"""
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Base class that defines data processing logic for a specific modality.
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In particular, we adopt a registry pattern to dispatch data processing
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according to the model being used (considering that different models may
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process the same data differently). This registry is in turn used by
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:class:`~MultiModalRegistry` which acts at a higher level
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(i.e., the modality of the data).
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"""
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def __init__(self) -> None:
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self._input_mappers = ClassRegistry[nn.Module, MultiModalInputMapper]()
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self._max_mm_tokens = ClassRegistry[nn.Module, MultiModalTokensCalc]()
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@abstractmethod
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def get_data_key(self) -> str:
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"""
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Get the data key corresponding to the modality.
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"""
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raise NotImplementedError
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@abstractmethod
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def _default_input_mapper(
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self,
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ctx: InputContext,
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data: ModalityData[Any],
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**mm_processor_kwargs,
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) -> MultiModalKwargs:
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"""
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Return a dictionary to be passed as keyword arguments to
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:meth:`~torch.nn.Module.forward`. This is similar in concept to
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tokenizers and processors in HuggingFace Transformers.
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If the data is not supported, throw :exc:`TypeError`.
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"""
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raise NotImplementedError
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def register_input_mapper(
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self,
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mapper: Optional[MultiModalInputMapper] = None,
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):
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"""
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Register an input mapper to a model class.
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When the model receives input data that matches the modality served by
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this plugin (see :meth:`get_data_key`), the provided function is
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invoked to transform the data into a dictionary of model inputs.
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If `None` is provided, then the default input mapper is used instead.
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"""
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def wrapper(model_cls: N) -> N:
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if self._input_mappers.contains(model_cls, strict=True):
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logger.warning(
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"Model class %s already has an input mapper "
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"registered to %s. It is overwritten by the new one.",
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model_cls,
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self,
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)
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self._input_mappers[model_cls] = (mapper
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or self._default_input_mapper)
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return model_cls
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return wrapper
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def map_input(
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self,
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model_config: "ModelConfig",
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data: ModalityData[Any],
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mm_processor_kwargs: Optional[dict[str, Any]],
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) -> MultiModalKwargs:
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"""
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Transform the data into a dictionary of model inputs using the
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input mapper registered for that model.
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The model is identified by ``model_config``.
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Raises:
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TypeError: If the data type is not supported.
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"""
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# Avoid circular import
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from vllm.model_executor.model_loader import get_model_architecture
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model_cls, _ = get_model_architecture(model_config)
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mapper = self._input_mappers.get(model_cls)
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if mapper is None:
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raise KeyError(f"No input mapper in {self} is registered for "
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f"model class {model_cls.__name__}.")
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if mm_processor_kwargs is None:
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mm_processor_kwargs = {}
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# In the case of the default mapper, we have to get resource
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# processor through its HuggingFace autoclass; since this goes
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# through **kwargs, we can't inspect it the same way, so we allow
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# drop mm_processor_kwargs based on signature inspection
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# if we're using the default mapper.
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#
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# This should be safe in general due to the sanitation, since the
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# transformers resource should filter unused kwargs anyway.
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uses_default_mapper = mapper == self._default_input_mapper
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mm_processor_kwargs = resolve_mm_processor_kwargs(
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model_config.mm_processor_kwargs,
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mm_processor_kwargs,
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callable=mapper,
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allow_var_kwargs=uses_default_mapper,
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)
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return mapper(InputContext(model_config), data, **mm_processor_kwargs)
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@abstractmethod
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def _default_max_multimodal_tokens(self, ctx: InputContext) -> int:
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"""
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Calculate the maximum number of tokens, corresponding to a single
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instance of multimodal data, that are passed to the language model.
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"""
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raise NotImplementedError
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def _validate_max_multimodal_tokens(self, max_mm_tokens: int):
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if max_mm_tokens < 1:
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raise ValueError("You should set the number of tokens to a "
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f"positive integer. Found: {max_mm_tokens}")
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def register_max_multimodal_tokens(
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self,
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max_mm_tokens: Optional[MultiModalTokensCalc] = None,
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):
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"""
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Register the maximum number of tokens, corresponding to a single
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instance of multimodal data, that are passed to the language model
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for a model class.
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If `None` is provided, then the default calculation is used instead.
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"""
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def wrapper(model_cls: N) -> N:
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if self._max_mm_tokens.contains(model_cls, strict=True):
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logger.warning(
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"Model class %s already calculates maximum number of "
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"tokens in %s. It is overwritten by the new one.",
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model_cls,
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self,
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)
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if isinstance(max_mm_tokens, int):
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self._validate_max_multimodal_tokens(max_mm_tokens)
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self._max_mm_tokens[model_cls] = (
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max_mm_tokens or self._default_max_multimodal_tokens)
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return model_cls
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return wrapper
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def get_max_multimodal_tokens(self, model_config: "ModelConfig") -> int:
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"""
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Get the maximum number of multi-modal tokens
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for profiling the memory usage of a model.
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If this registry is not applicable to the model, `0` is returned.
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The model is identified by ``model_config``.
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"""
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# Avoid circular import
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from vllm.model_executor.model_loader import get_model_architecture
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from vllm.model_executor.models import supports_multimodal
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model_cls, _ = get_model_architecture(model_config)
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if not supports_multimodal(model_cls):
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return 0
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max_mm_tokens = self._max_mm_tokens.get(model_cls)
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if max_mm_tokens is None:
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return 0
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if callable(max_mm_tokens):
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mm_processor_kwargs = get_allowed_kwarg_only_overrides(
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max_mm_tokens,
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overrides=model_config.mm_processor_kwargs,
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requires_kw_only=False,
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allow_var_kwargs=True,
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)
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max_mm_tokens = max_mm_tokens(InputContext(model_config),
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**mm_processor_kwargs)
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self._validate_max_multimodal_tokens(max_mm_tokens)
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return max_mm_tokens
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class MultiModalPlaceholderMap:
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"""
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Relates multi-modal embeddings to their corresponding placeholders.
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"""
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class IndexMap(NamedTuple):
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src: list[int]
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dest: list[int]
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src_ranges: list[range]
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"""
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The indices of the multi-modal embeddings that will replace the
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corresponding placeholder embeddings pointed to by ``dest_ranges``.
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"""
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src_len: int
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"""
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The total number of flattened multi-modal embeddings.
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"""
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dest_ranges: list[range]
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"""
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The indices of the placeholder embeddings that will be replaced by the
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multimodal embeddings.
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"""
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dest_len: int
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"""
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The total number of embeddings in the destination tensor.
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"""
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def __init__(self):
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self.src_ranges = []
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self.src_len = 0
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self.dest_ranges = []
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self.dest_len = 0
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@classmethod
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def from_seq_group(
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cls, seq_group: "SequenceGroupMetadata", positions: range
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) -> tuple[Optional[MultiModalDataDict], dict[str,
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"MultiModalPlaceholderMap"]]:
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"""
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Returns the multi-modal items that intersect with the portion of a
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prompt (``seq_group``) represented by ``positions``, as well as a
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``MultiModalPlaceholderMap`` that relates the multi-modal embedding
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vectors to their corresponding placeholders.
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Examples:
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.. code-block::
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Prompt: |AAAA BBBB What's in these images?|
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Positions: |.................................|
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images = [A, B]
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src_ranges = [(0, 4), (4, 8)]
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dest_ranges = [(0, 4), (5, 9)]
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Prompt: |AAAA BBBB What's in these images?|
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Positions: | ..... |
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images = [A, B]
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src_ranges = [(2, 4), (4, 6)]
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dest_ranges = [(0, 2), (3, 5)]
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Prompt: |AAAA BBBB What's in these images?|
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Positions: | ......... |
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images = [B]
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src_ranges = [(0, 4)]
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dest_ranges = [(0, 4)]
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Prompt: |AAAA BBBB What's in these images?|
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Positions: | .......................|
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images = []
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src_ranges = []
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dest_ranges = []
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"""
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seq_mm_data = seq_group.multi_modal_data
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seq_mm_placeholders = seq_group.multi_modal_placeholders
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if not seq_mm_data or not seq_mm_placeholders:
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return seq_mm_data, {}
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# For merged processor, we directly use mm_kwargs as mm_data
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if isinstance(seq_mm_data, MultiModalKwargs):
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placeholder_maps = dict[str, MultiModalPlaceholderMap]()
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for modality, placeholders in seq_mm_placeholders.items():
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placeholder_map = MultiModalPlaceholderMap()
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if positions:
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placeholder_map.append_items_from_seq_group(
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positions,
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# Dummy, since we don't care about intersecting items
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[None] * len(placeholders),
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placeholders,
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)
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placeholder_maps[modality] = placeholder_map
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return seq_mm_data, placeholder_maps
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mm_data = {**seq_mm_data}
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placeholder_maps = defaultdict[str, MultiModalPlaceholderMap](
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MultiModalPlaceholderMap)
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for modality, placeholders in seq_mm_placeholders.items():
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mm_items = mm_data.pop(modality)
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if not isinstance(mm_items, list):
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mm_items = [mm_items]
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if positions:
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intersecting_items = placeholder_maps[modality] \
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.append_items_from_seq_group(
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positions,
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mm_items,
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placeholders,
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)
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if intersecting_items:
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mm_data[modality] = intersecting_items
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return mm_data, placeholder_maps
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def append_items_from_seq_group(
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self,
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positions: range,
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multi_modal_items: list[_T],
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multi_modal_placeholders: Sequence[PlaceholderRange],
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) -> list[_T]:
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"""
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Adds the multi-modal items that intersect ```positions`` to this
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placeholder map and returns the intersecting items.
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"""
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intersecting_items = []
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if len(multi_modal_items) != len(multi_modal_placeholders):
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raise ValueError(
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"Multi-modal placeholders and items must have the same length."
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)
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for placeholder_dict, mm_item in zip(multi_modal_placeholders,
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multi_modal_items):
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placeholder = range(
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placeholder_dict["offset"],
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placeholder_dict["offset"] + placeholder_dict["length"],
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)
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intersection = range(
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max(positions.start, placeholder.start),
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min(positions.stop, placeholder.stop),
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)
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if not intersection:
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# Skip this multi-modal item.
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continue
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token_embedding_range = range(
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intersection.start - positions.start,
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intersection.stop - positions.start,
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)
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multimodal_embedding_range = range(
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intersection.start - placeholder.start + self.src_len,
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intersection.stop - placeholder.start + self.src_len,
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)
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intersecting_items.append(mm_item)
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self.dest_ranges.append(token_embedding_range)
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self.src_ranges.append(multimodal_embedding_range)
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self.src_len += len(placeholder)
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self.dest_len += len(positions)
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return intersecting_items
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def extend(self, other: "MultiModalPlaceholderMap"):
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"""
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Adds the placeholders from another ``MultiModalPlaceholderMap`` to this
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instance based on the source and destination tensors being
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concatenated.
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"""
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self.src_ranges.extend(
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range(self.src_len + r.start, self.src_len + r.stop)
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for r in other.src_ranges)
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self.src_len += other.src_len
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self.dest_ranges.extend(
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range(self.dest_len + r.start, self.dest_len + r.stop)
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for r in other.dest_ranges)
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self.dest_len += other.dest_len
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def index_map(self) -> "IndexMap":
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"""
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Finalizes the placeholder map into lists of indices that can be used to
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index the source and destination tensors.
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"""
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src_indices = [i for r in self.src_ranges for i in r]
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dest_indices = [i for r in self.dest_ranges for i in r]
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if len(src_indices) != len(dest_indices):
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raise ValueError(
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f"The number of source ({len(src_indices)}) and destination "
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f"indices ({len(dest_indices)}) must be the same.")
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return MultiModalPlaceholderMap.IndexMap(src=src_indices,
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dest=dest_indices)
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class MediaIO(ABC, Generic[_T]):
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@abstractmethod
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def load_bytes(self, data: bytes) -> _T:
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raise NotImplementedError
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@abstractmethod
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def load_base64(self, media_type: str, data: str) -> _T:
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"""
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List of media types:
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https://www.iana.org/assignments/media-types/media-types.xhtml
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"""
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raise NotImplementedError
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@abstractmethod
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def load_file(self, filepath: Path) -> _T:
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raise NotImplementedError
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